In this notebook, I examine the relationship between utterance fluency (UF) measures and L2 speaking anxiety, addressing the following three research questions (RQs):
RQ1: To what extent are speed fluency measures associated with cognitive, somatic, and behavioral speaking anxiety?
RQ2: To what extent are breakdown fluency measures associated with cognitive, somatic, and behavioral speaking anxiety?
RQ3: To what extent are repair fluency measures associated with cognitive, somatic, and behavioral speaking anxiety?
In the following sections, I preprocess data, conduct preliminary analyses, and construct regression models.
The following code block loads R packages required for analyses.
pacman::p_load(
tidyverse, here, skimr, psych,
PerformanceAnalytics, sjPlot, performance
)
This section tidies data. First of all, the following code block loads data of UF measures.
df_uf_raw <- read.csv(here("data", "processed", "uf_measures.csv"))
The current analysis focuses on the UF measures shown in Table 1.
Table 1. UF Dimension and Corresponding Measures
| UF Dimension | Measure |
|---|---|
| Speed Fluency (SF) | Articulation Rate (AR) |
| Breakdown Fluency (BDF) | Mid-Clause Pause Ratio (MCPR) |
| End-Clause Pause Ratio (ECPR) | |
| Mid-Clause Pause Duration (MCPD) | |
| End-Clause Pause Duration (ECPD) | |
| Repair Fluency (RF) | Disfluency Ratio |
Thus, the following code block selects target measures. The code
block also renames the columns filename and
dysfluency_ratio to participant_id and
disfluency_ratio, respectively.
df_uf_raw %>%
select(
filename,
articulation_rate,
mid_clause_pause_ratio,
end_clause_pause_ratio,
mid_clause_p.dur,
end_clause_p.dur,
dysfluency_ratio
) %>%
rename(
participant_id = filename,
disfluency_ratio = dysfluency_ratio
) -> df_uf_tidy
df_uf_tidy
Next, the following code block loads data of post questionnaire about anxiety and linguistic background.
df_questionnaire_raw <- read.csv(
here("data", "raw", "post_questionnaire.csv")
)
The following code block selects columns related to participant ID, anxiety, L2 English proficiency, and other backgrounds and renames them.
df_questionnaire_raw %>%
select(
参加者IDを入力してください.半角..,
英語を話すとき.私は言葉を間違えてしまうのではとしばしば心配になる.,
英語を話すとき.私は自分の話し方が他人より劣っているのではとしばしば心配になる.,
英語を話し始めるとすぐに.私は自分自身のことを表現できないのではと心配になり始める.,
英語を話すとき.私はドキドキすることがよくある.,
英語を話すとき.私は緊張でびくびくすることがよくある.,
英語を話すとき.私はよく汗をかいたり.発汗する.,
英語スピーキングの練習をするとき.私はしばしば.すぐに投げ出してしまう.,
私は英語スピーキングの練習の手を抜くことが多い.,
私は普段.極力英語を話さないようにしている.,
お持ちでしたら.英検のスコアを記入してください.,
お持ちでしたら.IELTS..Overall..のスコアを記入してください.,
お持ちでしたら.TOEFL.iBT.のスコアを記入してください.,
お持ちでしたら.TOEIC.のスコアを記入してください.,
性別,
年齢
) %>%
rename(
participant_id = 参加者IDを入力してください.半角..,
cognitive_anxiety_1 = 英語を話すとき.私は言葉を間違えてしまうのではとしばしば心配になる.,
cognitive_anxiety_2 = 英語を話すとき.私は自分の話し方が他人より劣っているのではとしばしば心配になる.,
cognitive_anxiety_3 = 英語を話し始めるとすぐに.私は自分自身のことを表現できないのではと心配になり始める.,
somatic_anxiety_1 = 英語を話すとき.私はドキドキすることがよくある.,
somatic_anxiety_2 = 英語を話すとき.私は緊張でびくびくすることがよくある.,
somatic_anxiety_3 = 英語を話すとき.私はよく汗をかいたり.発汗する.,
behavioral_anxiety_1 = 英語スピーキングの練習をするとき.私はしばしば.すぐに投げ出してしまう.,
behavioral_anxiety_2 = 私は英語スピーキングの練習の手を抜くことが多い.,
behavioral_anxiety_3 = 私は普段.極力英語を話さないようにしている.,
IELTS = お持ちでしたら.IELTS..Overall..のスコアを記入してください.,
TOEFL_IBT = お持ちでしたら.TOEFL.iBT.のスコアを記入してください.,
TOEIC = お持ちでしたら.TOEIC.のスコアを記入してください.,
eiken = お持ちでしたら.英検のスコアを記入してください.,
sex = 性別,
age = 年齢
) %>%
mutate(
sex = str_replace_all(sex, "男性", "male"),
sex = str_replace_all(sex, "女性", "female"),
eiken = str_replace_all(eiken, "準1級", "1.5"),
eiken = str_replace_all(eiken, "準1級", "1.5"),
eiken = str_replace_all(eiken, "2級", "2"),
eiken = str_replace_all(eiken, "1級", "1"),
eiken = str_replace_all(eiken, "英検4級", "4"),
eiken = as.numeric(eiken)
) -> df_questionnaire_tidy
df_questionnaire_tidy
The following code block calculates the descriptive statistics of participants’ age.
df_questionnaire_tidy %>%
select(age) %>%
skim()
| Name | Piped data |
| Number of rows | 10 |
| Number of columns | 1 |
| _______________________ | |
| Column type frequency: | |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| age | 0 | 1 | 28.8 | 5.61 | 20 | 26 | 29 | 30.5 | 38 | ▅▅▇▂▅ |
The result showed that participants’ age was ranged \([20, 38]\), and their mean and SD were \(28.8\) and \(5.613\).
The following code block counts sex of participants.
df_questionnaire_tidy %>%
group_by(sex) %>%
summarize(N = n())
The result indicated that there were equal number of male and female participants.
The following code block converts L2 English assessment scores to CEFR.
cefr_levels = c("A1", "A2", "B1", "B2", "C1", "C2")
df_questionnaire_tidy %>%
select(participant_id, TOEFL_IBT, IELTS, TOEIC, eiken) %>%
mutate(
TOEFL_IBT = case_when(
TOEFL_IBT >= 114 ~ "C2",
TOEFL_IBT >= 95 ~ "C1",
TOEFL_IBT >= 72 ~ "B2"
),
IELTS = case_when(
IELTS >= 7 ~ "C1"
),
TOEIC = case_when(
TOEIC >= 945 ~ "C1",
TOEIC >= 785 ~ "B1",
TOEIC >= 550 ~ "B2"
),
eiken = case_when(
eiken >= 3 ~ "A1",
eiken >= 2 ~ "B1",
eiken >= 1.5 ~ "B2",
eiken >= 1 ~ "C1"
)
) %>%
mutate(
TOEFL_IBT = factor(TOEFL_IBT, levels = cefr_levels, order = T),
IELTS = factor(IELTS, levels = cefr_levels, order = T),
TOEIC = factor(TOEIC, levels = cefr_levels, order = T),
eiken = factor(eiken, levels = cefr_levels, order = T)
) %>%
replace_na(list(
TOEFL_IBT = "A1",
IELTS = "A1",
TOEIC = "A1",
eiken = "A1"
)) %>%
group_by(participant_id) %>%
summarize(CEFR = max(TOEFL_IBT, IELTS, TOEIC, eiken)) %>%
group_by(CEFR) %>%
summarize(N = n())
The result suggested that most participants were advanced-level L2 English learners (\(N=6\)), while other four participants were intermediate-level English learners.
The following code block calculates the descriptive statistics of UF measures.
df_uf_tidy %>%
select(-c(participant_id)) %>%
skim()
| Name | Piped data |
| Number of rows | 10 |
| Number of columns | 6 |
| _______________________ | |
| Column type frequency: | |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| articulation_rate | 0 | 1 | 3.81 | 0.69 | 2.70 | 3.37 | 3.70 | 4.24 | 4.90 | ▂▇▇▂▅ |
| mid_clause_pause_ratio | 0 | 1 | 0.19 | 0.06 | 0.08 | 0.15 | 0.21 | 0.22 | 0.29 | ▂▅▁▇▂ |
| end_clause_pause_ratio | 0 | 1 | 0.07 | 0.01 | 0.05 | 0.06 | 0.06 | 0.07 | 0.09 | ▂▇▂▂▃ |
| mid_clause_p.dur | 0 | 1 | 0.76 | 0.26 | 0.44 | 0.58 | 0.69 | 0.89 | 1.25 | ▇▆▁▂▃ |
| end_clause_p.dur | 0 | 1 | 0.83 | 0.25 | 0.49 | 0.65 | 0.77 | 1.06 | 1.19 | ▅▇▂▂▇ |
| disfluency_ratio | 0 | 1 | 0.07 | 0.04 | 0.02 | 0.03 | 0.06 | 0.10 | 0.14 | ▇▂▃▃▂ |
The following code blocks calculate the reliability of anxiety questionnaire answers in terms of Cronbach’s \(\alpha\).
df_questionnaire_tidy %>%
select(
cognitive_anxiety_1,
cognitive_anxiety_2,
cognitive_anxiety_3
) %>%
alpha()
##
## Reliability analysis
## Call: alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.74 0.73 0.49 2.9 0.13 3.5 1.1 0.5
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.26 0.75 0.93
## Duhachek 0.49 0.75 1.00
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## cognitive_anxiety_1 0.81 0.83 0.71 0.71 4.89 0.11 NA
## cognitive_anxiety_2 0.65 0.66 0.50 0.50 1.97 0.21 NA
## cognitive_anxiety_3 0.42 0.42 0.27 0.27 0.73 0.37 NA
## med.r
## cognitive_anxiety_1 0.71
## cognitive_anxiety_2 0.50
## cognitive_anxiety_3 0.27
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## cognitive_anxiety_1 10 0.71 0.72 0.49 0.43 3.6 1.3
## cognitive_anxiety_2 10 0.81 0.81 0.72 0.60 3.7 1.3
## cognitive_anxiety_3 10 0.92 0.90 0.87 0.76 3.1 1.7
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## cognitive_anxiety_1 0.0 0.3 0.1 0.3 0.3 0
## cognitive_anxiety_2 0.1 0.1 0.0 0.6 0.2 0
## cognitive_anxiety_3 0.2 0.3 0.0 0.2 0.3 0
The Cornbach’s \(\alpha\) of cognitive anxiety questionnaires was \(.747\), indicating an acceptable internal consistency
df_questionnaire_tidy %>%
select(
somatic_anxiety_1,
somatic_anxiety_2,
somatic_anxiety_3
) %>%
alpha()
##
## Reliability analysis
## Call: alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.87 0.75 9.2 0.053 2.7 1.4 0.73
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.71 0.9 0.97
## Duhachek 0.80 0.9 1.00
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## somatic_anxiety_1 0.80 0.81 0.68 0.68 4.3 0.12 NA
## somatic_anxiety_2 0.84 0.84 0.73 0.73 5.3 0.10 NA
## somatic_anxiety_3 0.92 0.92 0.86 0.86 11.9 0.05 NA
## med.r
## somatic_anxiety_1 0.68
## somatic_anxiety_2 0.73
## somatic_anxiety_3 0.86
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## somatic_anxiety_1 10 0.94 0.94 0.92 0.87 2.9 1.5
## somatic_anxiety_2 10 0.94 0.92 0.89 0.83 2.8 1.7
## somatic_anxiety_3 10 0.86 0.88 0.76 0.73 2.5 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## somatic_anxiety_1 0.2 0.3 0.1 0.2 0.2 0
## somatic_anxiety_2 0.3 0.2 0.2 0.0 0.3 0
## somatic_anxiety_3 0.3 0.3 0.0 0.4 0.0 0
The Cornbach’s \(\alpha\) of somatic anxiety questionnaires was \(.900\), indicating a excellent internal consistency.
df_questionnaire_tidy %>%
select(
behavioral_anxiety_1,
behavioral_anxiety_2,
behavioral_anxiety_3
) %>%
alpha()
##
## Reliability analysis
## Call: alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.8 0.84 0.58 4.1 0.1 2.4 1.1 0.42
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.46 0.81 0.95
## Duhachek 0.61 0.81 1.01
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## behavioral_anxiety_1 0.53 0.55 0.38 0.38 1.2 0.283 NA
## behavioral_anxiety_2 0.58 0.59 0.42 0.42 1.4 0.260 NA
## behavioral_anxiety_3 0.97 0.97 0.94 0.94 30.8 0.021 NA
## med.r
## behavioral_anxiety_1 0.38
## behavioral_anxiety_2 0.42
## behavioral_anxiety_3 0.94
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## behavioral_anxiety_1 10 0.94 0.93 0.96 0.86 2.5 1.3
## behavioral_anxiety_2 10 0.93 0.91 0.95 0.81 2.8 1.4
## behavioral_anxiety_3 10 0.66 0.70 0.42 0.40 2.0 1.1
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## behavioral_anxiety_1 0.2 0.4 0.2 0.1 0.1 0
## behavioral_anxiety_2 0.2 0.3 0.1 0.3 0.1 0
## behavioral_anxiety_3 0.4 0.3 0.2 0.1 0.0 0
The Cornbach’s \(\alpha\) of somatic anxiety questionnaires was \(.814\), indicating a good internal consistency.
This subsection conducts correlation analyses between UF measures and speaking anxiety. Before the analyses, the following code block calculates three anxiety scores by summing items and joins the two dataframes.
df_questionnaire_tidy %>%
group_by(participant_id) %>%
summarize(
cognitive_anxiety = sum(
cognitive_anxiety_1, cognitive_anxiety_2, cognitive_anxiety_3
),
somatic_anxiety = sum(
somatic_anxiety_1, somatic_anxiety_2, somatic_anxiety_3
),
behavioral_anxiety = sum(
behavioral_anxiety_1, behavioral_anxiety_2, behavioral_anxiety_3
)
) %>%
inner_join(
df_uf_tidy, by = "participant_id"
) -> df_uf_anxiety
df_uf_anxiety
The following code block saves the concatenated dataframe as a csv file.
write.csv(df_uf_anxiety, here("data", "processed", "uf_anxiety.csv"))
The following code block generates a correlation matrix of three anxiety scores.
df_uf_anxiety %>%
select(
cognitive_anxiety,
somatic_anxiety,
behavioral_anxiety
) %>%
chart.Correlation(histogram = T, method = "pearson", pch = 19)
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
The result showed that significant strong correlation between cognitive and somatic anxiety (\(r=.78\)). In addition, there was slightly significant medium correlation between somatic and behavioral anxiety (\(r=.59\)). Meanwhile, there was small correlation between cognitive and behavioral anxiety (\(r=.48\)), but statistical significance was not found.
The following code block generates a correlation matrix of UF measures and cognitive anxiety.
df_uf_anxiety %>%
select(-c(
participant_id,
somatic_anxiety,
behavioral_anxiety
)) %>%
chart.Correlation(histogram = T, method = "pearson", pch = 19)
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
The result showed significant large correlation between cognitive anxiety and ECPR, suggesting that participants produced end-clause pauses more frequently when cognitive anxiety was lower.
Moreover, scatter plots between cognitive anxiety and AR, MCPD, and ECPD demonstrated parabolic patterns. More specifically, AR was slower when cognitive anxiety was lower or higher. In contrast, MCPD and ECPD was longer when cognitive anxiety was lower or higher.
The following code block generates a correlation matrix of UF measures and somatic anxiety.
df_uf_anxiety %>%
select(-c(
participant_id,
cognitive_anxiety,
behavioral_anxiety
)) %>%
chart.Correlation(histogram = T, method = "pearson", pch = 19)
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
The result did not show significant correlations between somatic anxiety and UF measures.
The following code block generates a correlation matrix of UF measures and behavioral anxiety.
df_uf_anxiety %>%
select(-c(
participant_id,
cognitive_anxiety,
somatic_anxiety
)) %>%
chart.Correlation(histogram = T, method = "pearson", pch = 19)
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
The result identified significant large correlations between behavioral anxiety and MCPD (\(r=-.76\)) and ECPD (\(r=.79\)), suggesting that participants who perceived lower behavioral anxiety generate longer MCPD and ECPD.
Given the parabolic relationship between cognitive anxiety and AR, MCPD, ECPD, I explore linear and quadratic relationship between them. To mitigate multicolinearity, the following code block centralizes anxiety scores.
df_uf_anxiety %>%
mutate(
cognitive_anxiety = cognitive_anxiety - mean(cognitive_anxiety),
somatic_anxiety = somatic_anxiety - mean(somatic_anxiety),
behavioral_anxiety = behavioral_anxiety - mean(behavioral_anxiety)
) -> df_uf_anxiety_centered
Given the parabolic relationship between AR and cognitive anxiety, I compare linear and quadratic models in terms of AIC and the reliability of models.
The following code block constructs a linear model between AR and speaking anxiety, where cognitive anxiety term is a linear.
model_ar_linear <- lm(
articulation_rate ~ cognitive_anxiety + somatic_anxiety + behavioral_anxiety,
df_uf_anxiety_centered
)
The following code block shows the result.
tab_model(model_ar_linear, digits = 3, show.aic = T)
| articulation_rate | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.814 | 3.198 – 4.431 | <0.001 |
| cognitive anxiety | -0.036 | -0.340 – 0.267 | 0.778 |
| somatic anxiety | -0.006 | -0.276 – 0.265 | 0.962 |
| behavioral anxiety | 0.081 | -0.172 – 0.333 | 0.464 |
| Observations | 10 | ||
| R2 / R2 adjusted | 0.104 / -0.344 | ||
| AIC | 28.729 | ||
The result did not show significant relationship between three anxiety scores and AR. In addition, AIC was \(28.729\).
To examine the reliability of the current model, the following code block conducts post-hoc analysis of regression assumptions.
check_model(model_ar_linear)
The results suggested that the linear model could be reliable.
The following code block constructs a quadratic model between AR and speaking anxiety, adding a quadratic cognitive anxiety term.
model_ar_quad <- lm(
articulation_rate ~ cognitive_anxiety + I(cognitive_anxiety^2) + somatic_anxiety + behavioral_anxiety,
df_uf_anxiety_centered
)
The following code block shows the result of the quadratic model.
tab_model(model_ar_quad, digits = 3, show.aic = T)
| articulation_rate | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.392 | 1.893 – 4.891 | 0.002 |
| cognitive anxiety | 0.007 | -0.349 – 0.364 | 0.960 |
| cognitive anxiety^2 | 0.040 | -0.086 – 0.166 | 0.455 |
| somatic anxiety | -0.064 | -0.411 – 0.283 | 0.655 |
| behavioral anxiety | 0.190 | -0.252 – 0.633 | 0.319 |
| Observations | 10 | ||
| R2 / R2 adjusted | 0.208 / -0.426 | ||
| AIC | 29.499 | ||
The result also showed that any anxiety did not significantly explain AR. AIC was \(29.499\), which was higher than the liner model, suggesting that the quadratic model is less-fit than the model linear.
The following code block conducts post-hoc analysis of the quadratic model.
check_model(model_ar_quad)
The results indicated that the quadratic model was less reliable because an outliner impact was big. Thus, I decided that the linear model is more representative of the association between AR and speaking anxiety though there was no significant relationship between them.
The following code block constructs a regression model between MCPR and speaking anxiety.
model_mcpr <- lm(
mid_clause_pause_ratio ~ cognitive_anxiety + somatic_anxiety + behavioral_anxiety,
df_uf_anxiety_centered
)
The following code block shows the result.
tab_model(model_mcpr, digits = 3, show.aic = T)
| mid_clause_pause_ratio | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.190 | 0.136 – 0.244 | <0.001 |
| cognitive anxiety | 0.006 | -0.020 – 0.033 | 0.580 |
| somatic anxiety | -0.000 | -0.024 – 0.024 | 0.999 |
| behavioral anxiety | -0.000 | -0.022 – 0.022 | 0.995 |
| Observations | 10 | ||
| R2 / R2 adjusted | 0.127 / -0.310 | ||
| AIC | -19.970 | ||
The result did not show significant relationship between three anxiety scores and MCPR.
To examine the reliability of the current model, the following code block conducts post-hoc analysis of regression assumptions.
check_model(model_mcpr)
The results suggested that the current model could be less reliable because the model could be affected by an outlier.
The following code block constructs a regression model between ECPR and speaking anxiety.
model_ecpr <- lm(
end_clause_pause_ratio ~ cognitive_anxiety + somatic_anxiety + behavioral_anxiety,
df_uf_anxiety_centered
)
The following code block shows the result.
tab_model(model_ecpr, digits = 3, show.aic = T)
| end_clause_pause_ratio | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.065 | 0.058 – 0.073 | <0.001 |
| cognitive anxiety | -0.003 | -0.007 – 0.001 | 0.081 |
| somatic anxiety | 0.001 | -0.002 – 0.004 | 0.458 |
| behavioral anxiety | -0.001 | -0.004 – 0.003 | 0.674 |
| Observations | 10 | ||
| R2 / R2 adjusted | 0.542 / 0.313 | ||
| AIC | -59.514 | ||
The result showed slightly significant relationship between cognitive anxiety and ECPR (\(p=.081\)). The slope was \(-.003\) and suggested that ECPR decreased \(.003\) when cognitive anxiety increased \(1\).
To examine the reliability of the current model, the following code block conducts post-hoc analysis of regression assumptions.
check_model(model_ecpr)
The results suggested that the current model could be reliable.
Since the scatter plot showed parabolic relationship, I explore two linear and quadratic models.
The following code block constructs a linear model between MCPD and speaking anxiety.
model_mcpd_linear <- lm(
mid_clause_p.dur ~ cognitive_anxiety + somatic_anxiety + behavioral_anxiety,
df_uf_anxiety_centered
)
The following code block shows the result.
tab_model(model_mcpd_linear, digits = 3, show.aic = T)
| mid_clause_p.dur | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.757 | 0.607 – 0.907 | <0.001 |
| cognitive anxiety | -0.014 | -0.088 – 0.060 | 0.655 |
| somatic anxiety | 0.026 | -0.040 – 0.092 | 0.375 |
| behavioral anxiety | -0.074 | -0.136 – -0.013 | 0.025 |
| Observations | 10 | ||
| R2 / R2 adjusted | 0.634 / 0.451 | ||
| AIC | 0.428 | ||
The result showed a significant link between behavioral anxiety and MCPD (\(p=.025\)). The slope was \(-.074\), suggesting that MCPD became \(.074\) shorter when behavioral anxiety score increased \(1\). In addition, AIC achieved \(.428\).
To examine the reliability of the linear model, the following code block conducts post-hoc analysis of regression assumptions.
check_model(model_mcpd_linear)
The results suggested that the current model could be reliable.
The following code block constructs a quadratic model.
model_mcpd_quad <- lm(
mid_clause_p.dur ~ cognitive_anxiety + I(cognitive_anxiety^2) + somatic_anxiety + behavioral_anxiety,
df_uf_anxiety_centered
)
The following code block showed the result.
tab_model(model_mcpd_quad, digits = 3, show.aic = T)
| mid_clause_p.dur | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.608 | 0.271 – 0.945 | 0.006 |
| cognitive anxiety | 0.001 | -0.079 – 0.081 | 0.968 |
| cognitive anxiety^2 | 0.014 | -0.014 – 0.042 | 0.260 |
| somatic anxiety | 0.005 | -0.073 – 0.083 | 0.873 |
| behavioral anxiety | -0.036 | -0.135 – 0.064 | 0.397 |
| Observations | 10 | ||
| R2 / R2 adjusted | 0.723 / 0.502 | ||
| AIC | -0.373 | ||
The results did not demonstrate significant predictors, while AIC was \(-.373\), which was lower than the linear model.
The following code block conducts post-hoc analysis of the quadratic model.
check_model(model_mcpd_quad)
The results showed the model was affected by an outlier. Since the current sample size was 10, the lower AIC could be caused by overfit. Thus, I determined the linear model as a representative model.
The following code block constructs a linear model between AR and speaking anxiety.
model_ecpd_linear <- lm(
end_clause_p.dur ~ cognitive_anxiety + somatic_anxiety + behavioral_anxiety,
df_uf_anxiety_centered
)
The following code block shows the result.
tab_model(model_ecpd_linear, digits = 3, show.aic = T)
| end_clause_p.dur | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.831 | 0.695 – 0.967 | <0.001 |
| cognitive anxiety | -0.005 | -0.072 – 0.062 | 0.872 |
| somatic anxiety | 0.017 | -0.043 – 0.077 | 0.511 |
| behavioral anxiety | -0.072 | -0.127 – -0.016 | 0.020 |
| Observations | 10 | ||
| R2 / R2 adjusted | 0.661 / 0.491 | ||
| AIC | -1.473 | ||
The result showed a significant link between behavioral anxiety and MCPD (\(p=.020\)). The slope was \(-.072\), suggesting that ECPD became \(.072\) shorter when behavioral anxiety score increased \(1\). AIC was \(-1.473\).
To examine the reliability of the current model, the following code block conducts post-hoc analysis of regression assumptions.
check_model(model_ecpd_linear)
The results suggested that the current model could be reliable.
The following code block constructs a linear model between AR and speaking anxiety.
model_ecpd_quad <- lm(
end_clause_p.dur ~ cognitive_anxiety + I(cognitive_anxiety^2) + somatic_anxiety + behavioral_anxiety,
df_uf_anxiety_centered
)
The following code block shows the result.
tab_model(model_ecpd_quad, digits = 3, show.aic = T)
| end_clause_p.dur | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.783 | 0.436 – 1.129 | 0.002 |
| cognitive anxiety | 0.000 | -0.082 – 0.083 | 0.990 |
| cognitive anxiety^2 | 0.005 | -0.025 – 0.034 | 0.704 |
| somatic anxiety | 0.010 | -0.070 – 0.091 | 0.753 |
| behavioral anxiety | -0.059 | -0.161 – 0.043 | 0.198 |
| Observations | 10 | ||
| R2 / R2 adjusted | 0.671 / 0.408 | ||
| AIC | 0.209 | ||
The result did not show significant predictor. Moreover, AIC was higher than the linear model (\(.209\)).
To examine the reliability of the quadratic model, the following code block conducts post-hoc analysis of regression assumptions.
check_model(model_ecpd_quad)
The results suggested that the current model could be less reliable than the linear model because of larger outlier impacts.
The following code block constructs a regression model between DR and speaking anxiety.
model_dr <- lm(
disfluency_ratio ~ cognitive_anxiety + somatic_anxiety + behavioral_anxiety,
df_uf_anxiety_centered
)
The following code block shows the result.
tab_model(model_dr, digits = 3, show.aic = T)
| disfluency_ratio | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.067 | 0.028 – 0.107 | 0.006 |
| cognitive anxiety | 0.003 | -0.016 – 0.022 | 0.712 |
| somatic anxiety | -0.003 | -0.020 – 0.014 | 0.661 |
| behavioral anxiety | 0.003 | -0.013 – 0.019 | 0.631 |
| Observations | 10 | ||
| R2 / R2 adjusted | 0.066 / -0.401 | ||
| AIC | -26.415 | ||
The result did not show significant relationship between three anxiety scores and DR.
To examine the reliability of the current model, the following code block conducts post-hoc analysis of regression assumptions.
check_model(model_dr)
The results suggested that the current model could be less reliable because the model could be affected by an outlier.